Reducing Uncertainty Caused by Neutral Online Ratings and an Overload of Online Reviews
(A2024-119908)

AUTHORS

Pradeep Kumar Ponnamma Divakaran, Rennes School of Business; Jie Xiong, ESSCA School of Management

ABSTRACT

Neutral or mixed online ratings and reviews are those that express neither positive nor negative sentiment about a product and are typically provided by customers who are neither fully satisfied nor dissatisfied with their purchase. Additionally, the abundance of online reviews for each product, numbering in the hundreds and thousands, poses a challenge for consumers, who cannot feasibly read each one. The question then arises as to how consumers can reduce uncertainty about a product's quality and performance, given the abundance of reviews and the prevalence of neutral ratings. To address this question, we draw from uncertainty reduction theory, information overload theory, and the elaboration likelihood model to argue that consumers can reduce uncertainty by seeking additional independent sources of information and engaging in systematic processing, but only for less familiar or unknown products. For popular products, consumers tend to rely on the peripheral route of processing, using heuristics such as reputation (positive or negative) to reduce uncertainty, without actively seeking additional information. Our study provides insights into how consumers navigate the challenges of neutral ratings and information overload to reduce uncertainty when evaluating new products.